Recommendation systems may be deployed in a variety of applications such as social networking sites (e.g., FACEBOOK), search engines (e.g., BING), video sharing sites (e.g., YOUTUBE), electronic commerce (e-commerce) sites (e.g., AMAZON.COM), and so forth, wherein content may be recommended to users based on the users' past online activity. Conventional solutions, however, may recommend content that is appropriate for a user from one perspective but inappropriate for the user from another perspective. For example, particular content might be recommended to a user due to the type of content (e.g., the content is associated with a favorite genre and/or subject matter of the user), whereas that content may be undesirable to the user from a social perspective (e.g., the content originates from a group of individuals disliked by the user). Additionally, content may be recommended to a user due to the social relevance of the content (e.g., the content originates from a favorite social group of the user), whereas the type of content might be undesirable from the user's perspective. Moreover, conventional systems may recommend content that is appropriate for a user at one moment in time but is inappropriate at another moment in time (e.g., due to the user's social setting and/or surroundings).